""" Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`
    - https://arxiv.org/pdf/2103.14030
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""

# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import logging
import math
from copy import deepcopy
from typing import Optional

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.fx_features import register_notrace_function
from timm.models.helpers import build_model_with_cfg, named_apply
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.layers import _assert
from timm.models.registry import register_model

from timm.models.vision_transformer import (
    checkpoint_filter_fn,
    get_init_weights_vit,
)

_logger = logging.getLogger(__name__)


class Mlp(nn.Module):
    """MLP as used in Vision Transformer, MLP-Mixer and related networks"""

    def __init__(
        self,
        in_features,
        hidden_features=None,
        out_features=None,
        act_layer=nn.GELU,
        bias=True,
        drop=0.0,
        tuning_mode=None,
    ):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        bias = to_2tuple(bias)
        drop_probs = to_2tuple(drop)

        self.fc1 = nn.Linear(
            in_features, hidden_features, bias=bias[0]
        )
        self.act = act_layer()
        self.drop1 = nn.Dropout(drop_probs[0])
        self.fc2 = nn.Linear(
            hidden_features, out_features, bias=bias[1]
        )
        self.drop2 = nn.Dropout(drop_probs[1])

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop1(x)
        x = self.fc2(x)
        x = self.drop2(x)

        return x


def window_partition(x, window_size: int):
    """
    Args:
        x: (B, H, W, C)
        window_size (int): window size
    Returns:
        windows: (num_windows*B, window_size, window_size, C)
    """
    B, H, W, C = x.shape
    x = x.view(
        B,
        H // window_size,
        window_size,
        W // window_size,
        window_size,
        C,
    )
    windows = (
        x.permute(0, 1, 3, 2, 4, 5)
        .contiguous()
        .view(-1, window_size, window_size, C)
    )
    return windows


@register_notrace_function  # reason: int argument is a Proxy
def window_reverse(windows, window_size: int, H: int, W: int):
    """
    Args:
        windows: (num_windows*B, window_size, window_size, C)
        window_size (int): Window size
        H (int): Height of image
        W (int): Width of image
    Returns:
        x: (B, H, W, C)
    """
    B = int(windows.shape[0] / (H * W / window_size / window_size))
    x = windows.view(
        B,
        H // window_size,
        W // window_size,
        window_size,
        window_size,
        -1,
    )
    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
    return x


class WindowAttention(nn.Module):
    r"""Window based multi-head self attention (W-MSA) module with relative position bias.
    It supports both of shifted and non-shifted window.
    Args:
        dim (int): Number of input channels.
        window_size (tuple[int]): The height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
    """

    def __init__(
        self,
        dim,
        window_size,
        num_heads,
        qkv_bias=True,
        attn_drop=0.0,
        proj_drop=0.0,
    ):

        super().__init__()
        self.dim = dim
        self.window_size = window_size  # Wh, Ww
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = head_dim**-0.5

        # define a parameter table of relative position bias
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros(
                (2 * window_size[0] - 1) * (2 * window_size[1] - 1),
                num_heads,
            )
        )  # 2*Wh-1 * 2*Ww-1, nH

        # get pair-wise relative position index for each token inside the window
        coords_h = torch.arange(self.window_size[0])
        coords_w = torch.arange(self.window_size[1])
        coords = torch.stack(
            torch.meshgrid([coords_h, coords_w])
        )  # 2, Wh, Ww
        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
        relative_coords = (
            coords_flatten[:, :, None] - coords_flatten[:, None, :]
        )  # 2, Wh*Ww, Wh*Ww
        relative_coords = relative_coords.permute(
            1, 2, 0
        ).contiguous()  # Wh*Ww, Wh*Ww, 2
        relative_coords[:, :, 0] += (
            self.window_size[0] - 1
        )  # shift to start from 0
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
        relative_position_index = relative_coords.sum(
            -1
        )  # Wh*Ww, Wh*Ww
        self.register_buffer(
            "relative_position_index", relative_position_index
        )

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        trunc_normal_(self.relative_position_bias_table, std=0.02)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x, mask: Optional[torch.Tensor] = None):
        """
        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        """
        B_, N, C = x.shape
        # if self.tuning_mode == 'ssf':
        #     #qkv = (self.qkv(x) * self.ssf_scale_1 + self.ssf_shift_1).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        #     qkv = (ssf_ada(self.qkv(x), self.ssf_scale_1, self.ssf_shift_1)).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        # else:
        qkv = (
            self.qkv(x)
            .reshape(B_, N, 3, self.num_heads, C // self.num_heads)
            .permute(2, 0, 3, 1, 4)
        )
        q, k, v = qkv.unbind(
            0
        )  # make torchscript happy (cannot use tensor as tuple)

        q = q * self.scale
        attn = q @ k.transpose(-2, -1)

        relative_position_bias = self.relative_position_bias_table[
            self.relative_position_index.view(-1)
        ].view(
            self.window_size[0] * self.window_size[1],
            self.window_size[0] * self.window_size[1],
            -1,
        )  # Wh*Ww,Wh*Ww,nH
        relative_position_bias = relative_position_bias.permute(
            2, 0, 1
        ).contiguous()  # nH, Wh*Ww, Wh*Ww

        attn = attn + relative_position_bias.unsqueeze(0)

        if mask is not None:
            nW = mask.shape[0]
            attn = attn.view(
                B_ // nW, nW, self.num_heads, N, N
            ) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N)
            attn = self.softmax(attn)
        else:
            attn = self.softmax(attn)

        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)

        x = self.proj(x)

        x = self.proj_drop(x)
        return x


class ChannelAdapter(nn.Module):
    def __init__(self, num_selection, channel_index, scale, scaleonx):
        super().__init__()

        ## original ICT
        self.scale = scale
        self.scaleonx = scaleonx
        self.adapt_linear = nn.Linear(num_selection, num_selection)
        self.channel_index = channel_index

        ## initialization
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=0.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    def forward(self, x):

        # print(self.channel_index)

        if self.scaleonx:
            x[..., self.channel_index] = x[
                ..., self.channel_index
            ] * self.scale + self.adapt_linear(
                x[..., self.channel_index]
            )
        else:
            x[..., self.channel_index] += (
                self.adapt_linear(x[..., self.channel_index])
                * self.scale
            )

        return x


class SwinTransformerBlock(nn.Module):
    r"""Swin Transformer Block.
    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resulotion.
        num_heads (int): Number of attention heads.
        window_size (int): Window size.
        shift_size (int): Shift size for SW-MSA.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float, optional): Stochastic depth rate. Default: 0.0
        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(
        self,
        dim,
        input_resolution,
        num_heads,
        window_size=7,
        shift_size=0,
        mlp_ratio=4.0,
        qkv_bias=True,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm,
        tuning_mode=None,
        channel_index=None,
        scale=0,
        scaleonx=None,
        topN=32,
    ):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        if min(self.input_resolution) <= self.window_size:
            # if window size is larger than input resolution, we don't partition windows
            self.shift_size = 0
            self.window_size = min(self.input_resolution)
        assert (
            0 <= self.shift_size < self.window_size
        ), "shift_size must in 0-window_size"

        self.norm1 = norm_layer(dim)
        self.attn = WindowAttention(
            dim,
            window_size=to_2tuple(self.window_size),
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            attn_drop=attn_drop,
            proj_drop=drop,
            tuning_mode=tuning_mode,
        )

        self.drop_path = (
            DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        )
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(
            in_features=dim,
            hidden_features=mlp_hidden_dim,
            act_layer=act_layer,
            drop=drop,
            tuning_mode=tuning_mode,
        )

        if self.shift_size > 0:
            # calculate attention mask for SW-MSA
            H, W = self.input_resolution
            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
            h_slices = (
                slice(0, -self.window_size),
                slice(-self.window_size, -self.shift_size),
                slice(-self.shift_size, None),
            )
            w_slices = (
                slice(0, -self.window_size),
                slice(-self.window_size, -self.shift_size),
                slice(-self.shift_size, None),
            )
            cnt = 0
            for h in h_slices:
                for w in w_slices:
                    img_mask[:, h, w, :] = cnt
                    cnt += 1

            mask_windows = window_partition(
                img_mask, self.window_size
            )  # nW, window_size, window_size, 1
            mask_windows = mask_windows.view(
                -1, self.window_size * self.window_size
            )
            attn_mask = mask_windows.unsqueeze(
                1
            ) - mask_windows.unsqueeze(2)
            attn_mask = attn_mask.masked_fill(
                attn_mask != 0, float(-100.0)
            ).masked_fill(attn_mask == 0, float(0.0))
        else:
            attn_mask = None

        self.register_buffer("attn_mask", attn_mask)

        self.tuning_mode = tuning_mode

        if tuning_mode == "sct_attn":
            self.sct_attn = ChannelAdapter(
                topN, channel_index, scale, scaleonx
            )

    def forward(self, x):
        H, W = self.input_resolution
        B, L, C = x.shape

        shortcut = x

        x = self.norm1(x)

        x = x.view(B, H, W, C)

        # cyclic shift
        if self.shift_size > 0:
            shifted_x = torch.roll(
                x,
                shifts=(-self.shift_size, -self.shift_size),
                dims=(1, 2),
            )
        else:
            shifted_x = x

        # partition windows
        x_windows = window_partition(
            shifted_x, self.window_size
        )  # nW*B, window_size, window_size, C
        x_windows = x_windows.view(
            -1, self.window_size * self.window_size, C
        )  # nW*B, window_size*window_size, C

        # W-MSA/SW-MSA
        attn_windows = self.attn(
            x_windows, mask=self.attn_mask
        )  # nW*B, window_size*window_size, C

        # merge windows
        attn_windows = attn_windows.view(
            -1, self.window_size, self.window_size, C
        )
        shifted_x = window_reverse(
            attn_windows, self.window_size, H, W
        )  # B H' W' C

        # reverse cyclic shift
        if self.shift_size > 0:
            x = torch.roll(
                shifted_x,
                shifts=(self.shift_size, self.shift_size),
                dims=(1, 2),
            )
        else:
            x = shifted_x
        x = x.view(B, H * W, C)

        # FFN
        x = shortcut + self.drop_path(x)

        ## our sct
        if self.tuning_mode == "sct_attn":
            x = self.sct_attn(x)

        x = x + self.drop_path(self.mlp(self.norm2(x)))

        return x


class PatchMerging(nn.Module):
    r"""Patch Merging Layer.
    Args:
        input_resolution (tuple[int]): Resolution of input feature.
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(
        self,
        input_resolution,
        dim,
        norm_layer=nn.LayerNorm,
        tuning_mode=None,
    ):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(4 * dim)
        self.tuning_mode = tuning_mode

    def forward(self, x):
        """
        x: B, H*W, C
        """
        H, W = self.input_resolution
        B, L, C = x.shape

        _assert(L == H * W, "input feature has wrong size")
        _assert(
            H % 2 == 0 and W % 2 == 0,
            f"x size ({H}*{W}) are not even.",
        )

        x = x.view(B, H, W, C)

        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C

        x = self.norm(x)

        x = self.reduction(x)

        return x

    def extra_repr(self) -> str:
        return f"input_resolution={self.input_resolution}, dim={self.dim}"

    def flops(self):
        H, W = self.input_resolution
        flops = H * W * self.dim
        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
        return flops


class BasicLayer(nn.Module):
    """A basic Swin Transformer layer for one stage.
    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        depth (int): Number of blocks.
        num_heads (int): Number of attention heads.
        window_size (int): Local window size.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
    """

    def __init__(
        self,
        dim,
        input_resolution,
        depth,
        num_heads,
        window_size,
        mlp_ratio=4.0,
        qkv_bias=True,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        norm_layer=nn.LayerNorm,
        downsample=None,
        use_checkpoint=False,
        tuning_mode=None,
        channel_index=None,
        scales=None,
        scaleonx=None,
        topN=96,
    ):

        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        # build blocks
        self.blocks = nn.ModuleList(
            [
                SwinTransformerBlock(
                    dim=dim,
                    input_resolution=input_resolution,
                    num_heads=num_heads,
                    window_size=window_size,
                    shift_size=(
                        0 if (i % 2 == 0) else window_size // 2
                    ),
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    drop=drop,
                    attn_drop=attn_drop,
                    drop_path=(
                        drop_path[i]
                        if isinstance(drop_path, list)
                        else drop_path
                    ),
                    norm_layer=norm_layer,
                    tuning_mode=tuning_mode[i],
                    channel_index=channel_index[i],
                    scale=scales[depth],
                    scaleonx=scaleonx,
                    topN=topN,
                )
                for i in range(depth)
            ]
        )

        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(
                input_resolution,
                dim=dim,
                norm_layer=norm_layer,
                tuning_mode=tuning_mode,
            )
        else:
            self.downsample = None

    def forward(self, x):
        for blk in self.blocks:
            if not torch.jit.is_scripting() and self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x)
            else:
                x = blk(x)
        if self.downsample is not None:
            x = self.downsample(x)
        return x

    def extra_repr(self) -> str:
        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"


class PatchEmbed(nn.Module):
    """2D Image to Patch Embedding"""

    def __init__(
        self,
        img_size=224,
        patch_size=16,
        in_chans=3,
        embed_dim=768,
        norm_layer=None,
        flatten=True,
        tuning_mode=None,
    ):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        self.img_size = img_size
        self.patch_size = patch_size
        self.grid_size = (
            img_size[0] // patch_size[0],
            img_size[1] // patch_size[1],
        )
        self.num_patches = self.grid_size[0] * self.grid_size[1]
        self.flatten = flatten
        self.norm_layer = norm_layer

        self.proj = nn.Conv2d(
            in_chans,
            embed_dim,
            kernel_size=patch_size,
            stride=patch_size,
        )
        self.norm = (
            norm_layer(embed_dim) if norm_layer else nn.Identity()
        )

    def forward(self, x):
        B, C, H, W = x.shape
        _assert(
            H == self.img_size[0],
            f"Input image height ({H}) doesn't match model ({self.img_size[0]}).",
        )
        _assert(
            W == self.img_size[1],
            f"Input image width ({W}) doesn't match model ({self.img_size[1]}).",
        )

        x = self.proj(x)
        if self.flatten:
            x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
        x = self.norm(x)
        return x


class SwinTransformer_sct(nn.Module):
    r"""Swin Transformer
        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
          https://arxiv.org/pdf/2103.14030
    Args:
        img_size (int | tuple(int)): Input image size. Default 224
        patch_size (int | tuple(int)): Patch size. Default: 4
        in_chans (int): Number of input image channels. Default: 3
        num_classes (int): Number of classes for classification head. Default: 1000
        embed_dim (int): Patch embedding dimension. Default: 96
        depths (tuple(int)): Depth of each Swin Transformer layer.
        num_heads (tuple(int)): Number of attention heads in different layers.
        window_size (int): Window size. Default: 7
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
        drop_rate (float): Dropout rate. Default: 0
        attn_drop_rate (float): Attention dropout rate. Default: 0
        drop_path_rate (float): Stochastic depth rate. Default: 0.1
        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
        patch_norm (bool): If True, add normalization after patch embedding. Default: True
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
    """

    def __init__(
        self,
        img_size=224,
        patch_size=4,
        in_chans=3,
        num_classes=1000,
        embed_dim=96,
        depths=(2, 2, 6, 2),
        num_heads=(3, 6, 12, 24),
        window_size=7,
        mlp_ratio=4.0,
        qkv_bias=True,
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=0.1,
        norm_layer=nn.LayerNorm,
        ape=False,
        patch_norm=True,
        use_checkpoint=False,
        weight_init="",
        tuning_mode=None,
        channel_index_dict=None,
        topN=32,
        scale=0,
        scaleonx=None,
        **kwargs,
    ):
        super().__init__()

        self.num_classes = num_classes
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.ape = ape
        self.patch_norm = patch_norm
        self.num_features = int(
            embed_dim * 2 ** (self.num_layers - 1)
        )
        self.mlp_ratio = mlp_ratio

        # split image into non-overlapping patches
        self.patch_embed = PatchEmbed(
            img_size=img_size,
            patch_size=patch_size,
            in_chans=in_chans,
            embed_dim=embed_dim,
            norm_layer=norm_layer if self.patch_norm else None,
            tuning_mode=tuning_mode,
        )
        num_patches = self.patch_embed.num_patches
        self.patch_grid = self.patch_embed.grid_size

        # absolute position embedding
        if self.ape:
            self.absolute_pos_embed = nn.Parameter(
                torch.zeros(1, num_patches, embed_dim)
            )
            trunc_normal_(self.absolute_pos_embed, std=0.02)
        else:
            self.absolute_pos_embed = None

        self.pos_drop = nn.Dropout(p=drop_rate)

        # stochastic depth
        dpr = [
            x.item()
            for x in torch.linspace(0, drop_path_rate, sum(depths))
        ]  # stochastic depth decay rule

        self.tuning_mode = tuning_mode
        # tuning_mode_list = [[tuning_mode] * depths[i_layer] for i_layer in range(self.num_layers)]
        tuning_mode_list = [
            [None] * 2,
            [None] * 2,
            [None] * 9 + [tuning_mode] * 9,
            [tuning_mode] * 2,
        ]

        # build layers
        layers = []
        for i_layer in range(self.num_layers):
            layers += [
                BasicLayer(
                    dim=int(embed_dim * 2**i_layer),
                    input_resolution=(
                        self.patch_grid[0] // (2**i_layer),
                        self.patch_grid[1] // (2**i_layer),
                    ),
                    depth=depths[i_layer],
                    num_heads=num_heads[i_layer],
                    window_size=window_size,
                    mlp_ratio=self.mlp_ratio,
                    qkv_bias=qkv_bias,
                    drop=drop_rate,
                    attn_drop=attn_drop_rate,
                    drop_path=dpr[
                        sum(depths[:i_layer]) : sum(
                            depths[: i_layer + 1]
                        )
                    ],
                    norm_layer=norm_layer,
                    downsample=(
                        PatchMerging
                        if (i_layer < self.num_layers - 1)
                        else None
                    ),
                    use_checkpoint=use_checkpoint,
                    tuning_mode=tuning_mode_list[i_layer],
                    channel_index=channel_index_dict["index"][
                        (
                            sum(depths[: i_layer + 1])
                            - depths[i_layer]
                        ) : sum(depths[: i_layer + 1])
                    ],
                    scale=scale,
                    scaleonx=scaleonx,
                    topN=topN,
                )
            ]
        self.layers = nn.Sequential(*layers)

        self.norm = norm_layer(self.num_features)
        self.avgpool = nn.AdaptiveAvgPool1d(1)
        self.head = (
            nn.Linear(self.num_features, num_classes)
            if num_classes > 0
            else nn.Identity()
        )

        if weight_init != "skip":
            self.init_weights(weight_init)

    @torch.jit.ignore
    def init_weights(self, mode=""):
        assert mode in ("jax", "jax_nlhb", "moco", "")
        if self.absolute_pos_embed is not None:
            trunc_normal_(self.absolute_pos_embed, std=0.02)
        head_bias = (
            -math.log(self.num_classes) if "nlhb" in mode else 0.0
        )
        named_apply(
            get_init_weights_vit(mode, head_bias=head_bias), self
        )

    @torch.jit.ignore
    def no_weight_decay(self):
        return {"absolute_pos_embed"}

    @torch.jit.ignore
    def no_weight_decay_keywords(self):
        return {"relative_position_bias_table"}

    def get_classifier(self):
        return self.head

    def reset_classifier(self, num_classes, global_pool=""):
        self.num_classes = num_classes
        self.head = (
            nn.Linear(self.num_features, num_classes)
            if num_classes > 0
            else nn.Identity()
        )

    def forward_features(self, x):
        x = self.patch_embed(x)
        if self.absolute_pos_embed is not None:
            x = x + self.absolute_pos_embed
        x = self.pos_drop(x)
        x = self.layers(x)
        x = self.norm(x)

        x = self.avgpool(x.transpose(1, 2))  # B C 1
        x = torch.flatten(x, 1)
        return x

    def forward(self, x):
        x = self.forward_features(x)
        x = self.head(x)
        return x


def _create_swin_transformer_sct(variant, pretrained=False, **kwargs):
    model = build_model_with_cfg(
        SwinTransformer_sct,
        variant,
        pretrained,
        pretrained_filter_fn=checkpoint_filter_fn,
        **kwargs,
    )

    return model


# @register_model
# def swin_base_patch4_window12_384(pretrained=False, **kwargs):
#     """ Swin-B @ 384x384, pretrained ImageNet-22k, fine tune 1k
#     """
#     model_kwargs = dict(
#         patch_size=4, window_size=12, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs)
#     return _create_swin_transformer('swin_base_patch4_window12_384', pretrained=pretrained, **model_kwargs)


# @register_model
# def swin_base_patch4_window7_224(pretrained=False, **kwargs):
#     """ Swin-B @ 224x224, pretrained ImageNet-22k, fine tune 1k
#     """
#     model_kwargs = dict(
#         patch_size=4, window_size=7, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs)
#     return _create_swin_transformer('swin_base_patch4_window7_224', pretrained=pretrained, **model_kwargs)


# @register_model
# def swin_large_patch4_window12_384(pretrained=False, **kwargs):
#     """ Swin-L @ 384x384, pretrained ImageNet-22k, fine tune 1k
#     """
#     model_kwargs = dict(
#         patch_size=4, window_size=12, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), **kwargs)
#     return _create_swin_transformer('swin_large_patch4_window12_384', pretrained=pretrained, **model_kwargs)


# @register_model
# def swin_large_patch4_window7_224(pretrained=False, **kwargs):
#     """ Swin-L @ 224x224, pretrained ImageNet-22k, fine tune 1k
#     """
#     model_kwargs = dict(
#         patch_size=4, window_size=7, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), **kwargs)
#     return _create_swin_transformer('swin_large_patch4_window7_224', pretrained=pretrained, **model_kwargs)


# @register_model
# def swin_small_patch4_window7_224(pretrained=False, **kwargs):
#     """ Swin-S @ 224x224, trained ImageNet-1k
#     """
#     model_kwargs = dict(
#         patch_size=4, window_size=7, embed_dim=96, depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24), **kwargs)
#     return _create_swin_transformer('swin_small_patch4_window7_224', pretrained=pretrained, **model_kwargs)


# @register_model
# def swin_tiny_patch4_window7_224(pretrained=False, **kwargs):
#     """ Swin-T @ 224x224, trained ImageNet-1k
#     """
#     model_kwargs = dict(
#         patch_size=4, window_size=7, embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24), **kwargs)
#     return _create_swin_transformer('swin_tiny_patch4_window7_224', pretrained=pretrained, **model_kwargs)


# @register_model
# def swin_base_patch4_window12_384_in22k(pretrained=False, **kwargs):
#     """ Swin-B @ 384x384, trained ImageNet-22k
#     """
#     model_kwargs = dict(
#         patch_size=4, window_size=12, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs)
#     return _create_swin_transformer('swin_base_patch4_window12_384_in22k', pretrained=pretrained, **model_kwargs)


# @register_model
# def swin_base_patch4_window7_224_in22k(pretrained=False, **kwargs):
#     """ Swin-B @ 224x224, trained ImageNet-22k
#     """
#     model_kwargs = dict(
#         patch_size=4, window_size=7, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs)
#     return _create_swin_transformer('swin_base_patch4_window7_224_in22k', pretrained=pretrained, **model_kwargs)


# @register_model
# def swin_large_patch4_window12_384_in22k(pretrained=False, **kwargs):
#     """ Swin-L @ 384x384, trained ImageNet-22k
#     """
#     model_kwargs = dict(
#         patch_size=4, window_size=12, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), **kwargs)
#     return _create_swin_transformer('swin_large_patch4_window12_384_in22k', pretrained=pretrained, **model_kwargs)


# @register_model
# def swin_large_patch4_window7_224_in22k(pretrained=False, **kwargs):
#     """ Swin-L @ 224x224, trained ImageNet-22k
#     """
#     model_kwargs = dict(
#         patch_size=4, window_size=7, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), **kwargs)
#     return _create_swin_transformer('swin_large_patch4_window7_224_in22k', pretrained=pretrained, **model_kwargs)


@register_model
def swin_base_patch4_window7_224_in22k_sct(
    pretrained=False, **kwargs
):
    """Swin-B @ 224x224, trained ImageNet-22k"""
    model_kwargs = dict(
        patch_size=4,
        window_size=7,
        embed_dim=128,
        depths=(2, 2, 18, 2),
        num_heads=(4, 8, 16, 32),
        **kwargs,
    )
    return _create_swin_transformer_sct(
        "swin_base_patch4_window7_224_in22k",
        pretrained=pretrained,
        **model_kwargs,
    )
